Rule-based classification of power quality disturbances using S-transform

Rodríguez, Alejandro; Aguado, José A.; Martín, Francisco; López, Javier; Muñoz, Francisco Javier Pastor; Ruiz, J.

doi:10.1016/j.epsr.2011.12.009

Cited by 93 publications

(42 citation statements)

References 23 publications

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“…Previous studies have carried out a lot of in-depth research on TFA of PQ signals, including Hilbert-Huang transform (HHT) [5,6], S-transform (ST) [7][8][9] and discrete wavelet transform (DWT) [10][11][12]. In the current research results, the environmental noise is the main factor which affects the PQ classification accuracy, especially in the distribution network.…”

Section: Introductionmentioning

confidence: 88%

“…In the current research results, the environmental noise is the main factor which affects the PQ classification accuracy, especially in the distribution network. ST has been proved to have good anti-noise abilities among all the TFA methods [7][8][9]. Feature extraction of PQ signals using ST and its improved form has been paid more and more attention.…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Huang

Cai

et al. 2016

Entropy

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Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Huang

Cai

et al. 2016

Entropy

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“…There are 1000 signals in each group whose SNR are 30~50 dB, 30 dB, 40 dB and 50 dB. Decision trees based on ST [9] and GST [10] are also made for comparison with MGST. The result for 30~50 dB is shown in Table 4 and the results of 30 dB, 40 dB and 50 dB are shown in Figure 8.…”

Section: The Search Of Optimized Threshold Via Modified Psomentioning

confidence: 99%

Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree

et al. 2015

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Abstract:In a microgrid, the distributed generators (DG) can power the user loads directly. As a result, power quality (PQ) events are more likely to affect the users. This paper proposes a Multiresolution Generalized S-transform (MGST) approach to improve the ability of analyzing and monitoring the power quality in a microgrid. Firstly, the time-frequency distribution characteristics of different types of disturbances are analyzed. Based on the characteristics, the frequency domain is segmented into three frequency areas. After that, the width factor of the window function in the S-transform is set in different frequency areas. MGST has different time-frequency resolution in each frequency area to satisfy the recognition requirements of different disturbances in each frequency area. Then, a rule-based decision tree classifier is designed. In addition, particle swarm optimization (PSO) is applied to extract the applicable features. Finally, the proposed method is compared with some others. The simulation experiments show that the new approach has better accuracy and noise immunity.

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“…An algorithm, based on Stockwell's transform, artificial neural network-based classifier and rule-based decision tree, is proposed. Some of the latest research on combined disturbance recognition can be found in [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform

Liu

Cui

2015

Entropy

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Aiming at the combined power quality +disturbance recognition, an automated recognition method based on wavelet packet entropy (WPE) and modified incomplete S-transform (MIST) is proposed in this paper. By combining wavelet packet Tsallis singular entropy, energy entropy and MIST, a 13-dimension vector of different power quality (PQ) disturbances including single disturbances and combined disturbances is extracted. Then, a ruled decision tree is designed to recognize the combined disturbances. The proposed method is tested and evaluated using a large number of simulated PQ disturbances and some real-life signals, which include voltage sag, swell, interruption, oscillation transient, impulsive transient, harmonics, voltage fluctuation and their combinations. In addition, the comparison of the proposed recognition approach with some existing techniques is made. The experimental results show that the proposed method can effectively recognize the single and combined PQ disturbances.

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Rule-based classification of power quality disturbances using S-transform

Cited by 93 publications

References 23 publications

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree

Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform

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